Beyond the Vibes: Vibe Coding Changed Who Can Build, Not How Software Should Be Built
In the last few years, vibe coding has taken center stage by changing who can build software, but not what it takes to build it well. It is a development style defined by natural language prompts, rapid iteration, and an emphasis on getting things working fast.
Powered by AI-assisted tools and accessible platforms, vibe coding has genuinely democratized building . Startups, solo devs, and even non-technical founders can now create prototypes in hours, not months. That’s worth celebrating.
But as the hype grows, an important distinction is getting lost in the noise.
We’re starting to confuse vibe coding with software engineering.
And while they both involve code, they serve very different purposes and come with very different risks.
Where Vibe Coding Shines
Vibe coding works best when you’re:
- Testing an idea
- Prototyping fast
- Building internal tools
- Exploring creatively
It accelerates iteration and lowers the cost of experimentation. It’s a massive enabler for innovation, especially in early-stage product work.
The market agrees. According to Roots Analysis, the global vibe coding market is expected to grow from $2.96B in 2025 to $325B by 2040 - a 36.79% CAGR.
But the faster something grows, the more important it becomes to ask:
Is this still the right tool for the job?
The Foundations Vibe Coding Often Skips
What vibe coding often skips, and what experienced developers obsess over, are the foundations that keep systems standing:
- Clear, stable requirements
- Non-functional constraints (scale, security, latency)
- Architectural boundaries
- Testing strategies
- Maintainability
- Long-term risk
The Most Expensive Problems Don’t Show Up in Demos
In vibe coding, it’s easy to build something that feels finished, but ultimately collapses when it’s time to expose it to real users, real load, or when it’s time to scale. We’ve seen projects that look great on the surface but require complete rewrites just to support users, integrate with systems, or handle basic growth.
It’s not a failure of intent but a misunderstanding of complexity.
Traditional Engineering Brings Weight
Professional software development brings structure and, with it, intentional weight:
- It’s more expensive
- It takes longer
- It often requires external talent (agencies, architects, senior engineers)
- And it can feel heavy for early-stage work
But when the goal is durability, this is the discipline that delivers it. You’re building something to last. You need it to handle change, load, integration, regulation - things that don’t show up in a prototype demo.
Still, this is where many builders get stuck: cost and speed.
That's where many builders hit a wall.
A New Middle Ground: Orchestrated Multi-Agent Systems
So, what comes next?
We believe the next evolution isn’t about choosing between speed or structure, it’s about deliberately combining both.
Enter multi-agent systems (MAS). Autonomous agents that specialize in different aspects of the software lifecycle (planning, architecture, coding, testing, optimization).
Without Orchestration, AI Just Scales Chaos
Crucially, the breakthrough isn't the agents themselves. It's in the orchestration layer.
Without orchestration, agents operate in silos.
With orchestration, they act like a coordinated engineering team.
What MAS Orchestration Enables:
- Sequenced collaboration across AI agents (e.g. planner → coder → reviewer → tester)
- Integrated workflows across tools, platforms, and services
- Parallel execution to reduce latency and speed up delivery
- Maintainability through modular agent updates without breaking the system
- Smarter fallback and reliability mechanisms (e.g. retries, circuit breakers, role reassignment)
In short: orchestration turns "vibe" into "system".
We Use This Because We Want To Ship
At Brunelly, we didn’t adopt orchestration as a theory. We use it because we have to ship real systems. Our CTO refers to LLMs as “a slightly messier version of me.” And that is impressive.
If you want to read more about Brunelly’s orchestration, check out our CTO’s Guy Powell’s Substack.
Or if you prefer to test it out for yourself, feel free it’s live!
Three Phases of Modern Software Building
As we move into 2026, here’s the shift we see:
You Don’t Need Extremes. You Need Intent.
You don’t need to abandon vibe coding or overinvest in full-stack teams before you’re ready.
But if you're trying to build something credible and scalable, and you're looking for that elusive balance between speed and structure, multi-agent orchestration may offer a smarter third path.
Final Thought: Speed Is Optional. Clarity Isn’t.
The real question isn’t whether vibe coding is “good” or “bad.”
The question is: What are you building, and what will it take to get it there?
If you're testing the waters, move fast and explore.
If you're building the backbone of a product or company, slow down, think deeply, and choose the right system.
And 2026 is going to reward the teams who can do both intelligently.
Cold outreach scales linearly - same effort per reply every week. It's necessary for early traction but the founders who get to $10k+ MRR almost always layer in a compounding channel underneath it. SEO, community, partnerships, or product-led growth.
What's the channel you're betting on to build independently of your outreach?
It also changed how fast software can be built!
interesting framing but i think there's a piece missing between "vibe coding" and "professional software engineering" - it's the interaction design. how you actually talk to the AI, what the feedback loop feels like physically.
i've been running claude code in terminal for months and noticed something: the actual coding part shrank to like five repeated actions. the thinking happens in natural language, the execution is just accept/reject/scroll. so i ended up mapping those to a game controller (VibePad). wild? sure. but it forced me to think about what the human is actually doing in an AI coding session. and the answer is: not much typing.
the multi-agent orchestration stuff you mention is interesting for the software side. i think the hardware/input side is equally underexplored.
That’s not wild at all, it’s actually a very sharp observation!
What you’re describing is the cognitive shift that AI-assisted development creates. The bottleneck moves from syntax production to intent articulation and evaluation. The mechanical act of typing shrinks. The dominant loop becomes: specify → review → adjust → accept. That’s a fundamentally different human role than traditional coding.
Most people are still thinking in terms of “AI writes code faster.” The more interesting layer is that the human is transitioning from operator to director. If that’s true, then interaction design absolutely matters, because the friction isn’t in keystrokes anymore, it’s in how clearly you can express constraints and how efficiently you can audit output.
Where orchestration fits into this is at a different level. It structures how the AI collaborates with itself. What you’re exploring structures how the human collaborates with the AI. Both are coordination problems: one intra-system, one human–machine.
If the execution loop really collapses to a handful of actions, then optimizing physical interaction could materially improve cognitive flow. That’s not gimmicky — it’s acknowledging that the developer’s role has changed. The tooling layer, input devices, terminal UX, feedback mechanisms, is probably underexplored because we’re still mentally anchored in IDE-era interaction models.
You’re essentially prototyping what “post-keyboard” development might feel like. That’s worth paying attention to.
Vibe coding’s 36.79% CAGR growth is impressive, but your point about skipping foundations is valid.
I spent 8 months building a SaaS with no paying customers because I ignored real demand and long-term risks.
Always validate demand before building, even with fast prototyping tools. Balance speed with basic maintainability to avoid rewrites. Consider orchestration or structure early for durability, not just quick wins.
How does MAS orchestration fit into early validation without slowing things down?
That’s the right tension to explore.
Early validation shouldn’t feel like you’re building a distributed AI engineering department. If you’re still testing demand, MAS orchestration doesn’t mean spinning up a complex multi-agent pipeline on day one. It means introducing intentional structure at the points where failure would distort your learning.
In very early stages, you can keep it lightweight: one planning pass, one generation pass, one validation pass. Even that small amount of sequencing prevents you from validating against broken assumptions or fragile code. The goal isn’t durability yet, it’s signal clarity.
Where MAS becomes powerful is when you transition from “does anyone care?” to “can this survive real usage?” At that point, having discrete roles: planner, implementer, reviewer, tester, prevents local optimization from corrupting the system. You’re still moving fast, but you’re no longer flying blind.
So I’d frame it like this: early validation optimizes for speed of learning. Orchestration optimizes for quality of learning. You don’t need full structural weight on day one, but introducing review gates and role separation earlier than most people think will save you from validating the wrong thing.
I think vibe coding didn't really change who can build. Only developers can really vibe code. The difference is the time to market. Whoever vibcodes need to review the code either way to achieve its goal and make it maitainable. It is in fact quite usual to get to a point where the codebase becomes unexploitable by the ai, and any further prompt makes it worse. From my experience, there needs to be refactoring tasks requested to the llm, on a regular basis, otherwise things get out of hand. IDEs are very well equipped to deliver a feature, but they don't have the full context in memory to know how to organize the code effectively. Of course if we include platforms like lovable, bolt or base44 in the equation, i agree it enable non technical to play with code, but to me vibecoding is the process of generating code and reviewing it from an IDE.
I think you’re highlighting something important. There are really two different phenomena being lumped together under “vibe coding.”
If we define it strictly as prompting inside an IDE and reviewing the output, then yes, it’s still a developer activity. You need architectural intuition to spot when abstractions are leaking, when cohesion is breaking down, when coupling is creeping in. The LLM accelerates production, but it doesn’t replace judgment.
Where I’d push back slightly is that it did expand who can get to a functional artifact. Non-technical founders can now assemble working systems via higher-level platforms. That doesn’t mean they’re building maintainable software, but it does mean the barrier to producing something runnable has shifted meaningfully.
Your point about entropy is exactly the structural risk. LLMs optimize locally. They don’t maintain a global mental model of the evolving codebase unless you explicitly force that process. Without periodic refactoring passes, architectural consolidation, and constraint reinforcement, you accumulate invisible debt quickly. And once the system grows past what the model can reliably reason about in context, each new prompt starts degrading coherence rather than improving it.
This is less about vibe coding being “good” or “bad” and more about recognizing that generation without orchestration leads to architectural drift. Refactoring cycles, explicit design constraints, modular boundaries, those aren’t optional overhead. They’re the mechanism that prevents accelerated chaos.
Speed improved dramatically. Engineering discipline did not become optional.